The objective of this study was to analyze differences in thermoregulation and water balance under conditions of heat load and water restriction between fat-tailed sheep (S) and Kacang goats (G). The daily intakes of food and water, daily outputs of urine and feces, rectal temperature, respiration rates, hematocrit values and plasma volumes of five shorn S and five G were determined over 10 days of four consecutive experimental conditions: (1) indoor – unrestricted water; (2) indoor – restricted water; (3) 10 h sunlight exposure – unrestricted water; and (4) 10 h sunlight exposure – restricted water. There was a 6- to 7-day adjustment period between two consecutive conditions. The study was conducted during the dry season. The animals were placed in individual cages, fed chopped native grass ad libitum and had free access to a urea–molasses multi-nutrient block. Under sunlight exposure with unrestricted water availability, S and G record an increase in the maximum rectal temperatures from 39.2°C to 40.2°C and from 39.9°C to 41.8°C, respectively. The thermoregulatory strategy used by S for maintaining a lower rectal temperature mostly depends on increasing the respiration rate as the main cooling mechanism. On the other hand, G apparently used sweating as the predominant mechanism for cooling. Moreover, G seemed to be more tolerable to higher heat storage and body temperature than S with a significant increase in plasma volume (P < 0.01), and this may be beneficial to the animals for the prevention of water loss. Under restricted water condition in either indoor or outdoor environment, both species decreased their plasma volume significantly, but rectal temperatures were relatively maintained. In all experimental conditions, the daily total water exchanges (ml/kg0.82 per day) of S were significantly higher than G (P < 0.01). However, when the percentages of the total daily water exchange were considered, the water lost through urination (38% to 39%), defecation (11% to 14%) and evaporation (46% to 49%) by S and G was not significantly different. Therefore, the results from this study clearly showed that S and G have different homeostatic strategies for the regulation of body temperature and fluid to cope with heat load and water restriction. These differences may have an important impact on the production management of S and G.

Bayesian regularization of artificial neural networks (BRANNs) were used to predict body mass index (BMI) in mice using single nucleotide polymorphism (SNP) markers. Data from 1896 animals with both phenotypic and genotypic (12 320 loci) information were used for the analysis. Missing genotypes were imputed based on estimated allelic frequencies, with no attempt to reconstruct haplotypes based on family information or linkage disequilibrium between markers. A feed-forward multilayer perceptron network consisting of a single output layer and one hidden layer was used. Training of the neural network was done using the Bayesian regularized backpropagation algorithm. When the number of neurons in the hidden layer was increased, the number of effective parameters, γ, increased up to a point and stabilized thereafter. A model with five neurons in the hidden layer produced a value of γ that saturated the data. In terms of predictive ability, a network with five neurons in the hidden layer attained the smallest error and highest correlation in the test data although differences among networks were negligible. Using inherent weight information of BRANN with different number of neurons in the hidden layer, it was observed that 17 SNPs had a larger impact on the network, indicating their possible relevance in prediction of BMI. It is concluded that BRANN may be at least as useful as other methods for high-dimensional genome-enabled prediction, with the advantage of its potential ability of capturing non-linear relationships, which may be useful in the study of quantitative traits under complex gene action.

Knowledge of the genetic architecture of a quantitative trait is useful to adjust methods for the prediction of genomic breeding values and to discover the extent to which common assumptions in quantitative trait locus (QTL) mapping experiments and breeding value estimation are violated. It also affects our ability to predict the long-term response of selection. In this paper, we focus on additive and dominance effects of QTL. We derive formulae that can be used to estimate the number of QTLs that affect a quantitative trait and parameters of the distribution of their additive and dominance effects from variance components, inbreeding depression and results from QTL mapping experiments. It is shown that a lower bound for the number of QTLs depends on the ratio of squared inbreeding depression to dominance variance. That is, high inbreeding depression must be due to a sufficient number of QTLs because otherwise the dominance variance would exceed the true value. Moreover, the second moment of the dominance coefficient depends only on the ratio of dominance variance to additive variance and on the dependency between additive effects and dominance coefficients. This has implications on the relative frequency of overdominant alleles. It is also demonstrated how the expected number of large QTLs determines the shape of the distribution of additive effects. The formulae are applied to milk yield and productive life in Holstein cattle. Possible sources for a potential bias of the results are discussed.

Dairy herd size is expected to increase in many European countries, given the recent policy changes within the European Union. Managing more cows may have implications for herd performance in the post-quota era. The objective of this study was to characterise spring-calving herds according to size and rate of expansion, and to determine trends in breeding policy, reproduction and production performance, which will inform industry of the likely implications of herd expansion. Performance data from milk recording herds comprising 775 795 lactations from 2555 herds for the years 2004 to 2008 inclusive were available from the Irish Cattle Breeding Federation. Herds were classified into Small (average of 37 cows), Medium (average of 54 cows) and Large (average of 87 cows) and separately into herds that were not expanding (Nil expansion), herds expanding on average by three cows per year (Slow expansion) and herds expanding on average by eight cows per year (Rapid expansion). There was no association between rate of expansion and 305-day fat and protein yield. However, 305-day milk yield decreased and milk protein and fat percentage increased with increasing rate of expansion. There were no associations between herd size and milk production except for protein and fat percentage, which increased with increasing herd size. Average parity number of the cows decreased as rate of expansion increased and tended to decrease as herd size increased. In rapidly expanding herds, cow numbers were increased by purchasing more cattle. The proportion of dairy sires relative to beef sires used in the breeding programme of expanding herds increased and there was more dairy crossbreeding, albeit at a low rate. Similarly, large herds were using more dairy sires and fewer beef sires. Expanding herds and large herds had superior reproductive performance relative to non-expanding and small herds. Animals in expanding herds calved for the first time at a younger age, had a shorter calving interval and were submitted for breeding by artificial insemination at a higher rate. The results give confidence to dairy producers likely to undergo significant expansion post-quota such that, despite managing more cows, production and reproductive performance need not decline. The management skills required to achieve these performance levels need investigation.

Artificial-selection experiments constitute an important source of empirical information for breeders, geneticists and evolutionary biologists. Selected characters can generally be shifted far from their initial state, sometimes beyond what is usually considered as typical inter-specific divergence. A careful analysis of the data collected during such experiments may thus reveal the dynamical properties of the genetic architecture that underlies the trait under selection. Here, we propose a statistical framework describing the dynamics of selection-response time series. We highlight how both phenomenological models (which do not make assumptions on the nature of genetic phenomena) and mechanistic models (explaining the temporal trends in terms of e.g. mutations, epistasis or canalization) can be used to understand and interpret artificial-selection data. The practical use of the models and their implementation in a software package are demonstrated through the analysis of a selection experiment on the shape of the wing in Drosophila melanogaster.

Government policies relating to red meat production take account of the carbon footprint, environmental impact, and contributions to human health and nutrition, biodiversity and food security. This paper reviews the impact of grazing on these parameters and their interactions, identifying those practices that best meet governments’ strategic goals. The recent focus of research on livestock grazing and biodiversity has been on reducing grazing intensity on hill and upland areas. Although this produces rapid increases in sward height and herbage mass, changes in structural diversity and plant species are slower, with no appreciable short-term increases in biodiversity so that environmental policies that simply involve reductions in numbers of livestock may not result in increased biodiversity. Furthermore, upland areas rely heavily on nutrient inputs to pastures so that withdrawal of these inputs can threaten food security. Differences in grazing patterns among breeds increase our ability to manage biodiversity if they are matched appropriately to different conservation grazing goals. Lowland grassland systems differ from upland pastures in that additional nutrients in the form of organic and inorganic fertilisers are more frequently applied to lowland pastures. Appropriate management of these nutrient applications is required, to reduce the associated environmental impact. New slurry-spreading techniques and technologies (e.g. the trailing shoe) help reduce nutrient losses but high nitrogen losses from urine deposition remain a key issue for lowland grassland systems. Nitrification inhibitors have the greatest potential to successfully tackle this problem. Greenhouse gas (GHG) emissions are lower from indoor-based systems that use concentrates to shorten finishing periods. The challenge is to achieve the same level of performance from grass-based systems. Research has shown potential solutions through the use of forages containing condensed tannins or establishing swards with a high proportion of clover and high-sugar grasses. Relative to feeding conserved forage or concentrates, grazing fresh grass not only reduces GHG emissions but also enhances the fatty acid composition of meat in terms of consumer health. It is possible to influence biodiversity, nutrient utilisation, GHG emissions and the nutritional quality of meat in grass-based systems, but each of these parameters is intrinsically linked and should not be considered in isolation. Interactions between these parameters must be considered carefully when policies are being developed, in order to ensure that strategies designed to achieve positive gains in one category do not lead to a negative impact in another. Some win–win outcomes are identified.

The aim of this experiment was to evaluate the impact of selection for greater muscling on whole body insulin responsiveness in cattle, as reflected by greater uptake of glucose in response to constant insulin infusion and greater glucose disappearance following an intravenous glucose tolerance test. This study used 18-month-old steers from an Angus herd visually assessed and selected for divergence in muscling over 15 years. Eleven high-muscled (High), 10 low-muscled (Low) and 3 high-muscled steers, which were heterozygous for a myostatin polymorphism (HighHet), were infused with insulin using the hyperinsulineamic-euglyceamic clamp technique. Insulin was constantly infused at two levels, 0.6 μIU/kg per min and 6.0 μIU/kg per min. Glucose was concurrently infused to maintain euglyceamia and the steady state glucose infusion rate (SSGIR) indicated insulin responsiveness. An intravenous glucose tolerance test was also administered at 200 mg/kg live weight. Sixteen blood samples were collected from each animal between −30 and 130 min relative to the administration of intravenous glucose, plasma glucose and insulin concentration was determined in order to analyse insulin secretion and glucose disappearance. Insulin-like growth factor-1 (IGF-1) was also measured in basal plasma samples. At the low insulin infusion rate of 0.6 mU/kg per min, the SSGIR was 73% higher for the High muscling genotype animals when compared to the Low (P < 0.05). At the high insulin infusion rate of 6.0 mU/kg per min, these differences were proportionately less with the High and the HighHet genotypes having only 27% and 34% higher SSGIR (P < 0.05) than the Low-muscled genotype. The High-muscled cattle also had 30% higher plasma IGF-1 concentrations compared to the Low-muscled cattle. There was no effect of muscling genotype on basal insulin or basal glucose concentrations, glucose disappearance or insulin secretion following an intravenous glucose tolerance test. The increased whole body insulin responsiveness in combination with higher IGF-1 concentrations in the High-muscled steers is likely to initiate a greater level of protein synthesis, which may partially explain the increased muscle accretion in these animals.

Subcutaneous fat from Norwegian Landrace (n = 3230) and Duroc (n = 1769) pigs was sampled to investigate the sources of variation and genetic parameters of various fatty acids, fat moisture percentage and fat colour, with the lean meat percentage (LMP) also included as a trait representing the leanness of the pig. The pigs were from half-sib groups of station-tested boars included in the Norwegian pig breeding scheme. They were fed ad libitum to obtain an average of 113 kg live weight. Near-infrared spectroscopy (NIRS) was applied for prediction of the fatty acids and fat moisture percentage, and Minolta was used for the fat colour measurements. Heritabilities and genetic correlations were estimated with a multi-trait animal model using average information-restricted maximum likelihood (AI-REML) methodology. Fat from Landrace pigs had considerably more monounsaturated fatty acids, polyunsaturated fatty acids (PUFAs) and fat moisture, as well as less saturated fatty acids (SFAs) than fat from Duroc pigs. The heritability estimates (s.e. 0.03 to 0.08) for the various fatty acids were as follows: Palmitic, C16:0 (0.39 and 0.51 for Landrace and Duroc pigs, respectively); Palmitoleic, C16:1n-7 (0.41 and 0.50); Steric, C18:0 (0.46 and 0.54); Oleic, C18:1n-9 (0.67 and 0.57); Linoleic, C18:2n-6 (0.44 and 0.46); α-linolenic, C18:3n-3 (0.37 and 0.25) and n-6/n-3 ratio (0.06 and 0.01). The other fat quality traits revealed the following heritabilities: fat moisture (0.28 and 0.33), colour values in subcutaneous fat: L* (whiteness; 0.22 and 0.21), a* (redness; 0.13 and 0.24) and b* (yellowness; 0.07 and 0.17) and LMP (0.46 and 0.47). LMP showed high positive genetic correlations to PUFA (C18:2n-6 and C18:3n-3), which implies that selecting leaner pigs changes the fatty acid composition and deteriorates the quality of fat. Higher concentrations of PUFA are not beneficial as the ratio of n-6 and n-3 fatty acids becomes unfavourably high. Owing to the high genetic correlation between C18:2n-6 and C18:3n-3 and a low heritability for this ratio, the latter is difficult to change through selection. However, a small reduction in the ratio should be expected if selection aims at reducing the level of C18:2n-6. Selection for more C18:1n-9 is possible in view of the genetic parameters, which are favourable for eating quality, technological quality and human nutrition. The NIRS technology and the high heritabilities found in this study make it possible to implement fat quality traits to achieve the breeding goal in the selection of a lean pig with better fat quality.

Reproductive biotechnology such as in vitro fertilization, the creation of transgenic animals or cloning by nuclear transfer depends on the use of fully grown, meiotically competent oocytes capable of completing meiotic maturation by reaching the stage of metaphase II. However, there exists only a limited quantity of these oocytes in the ovaries of females. In view of their limited number, growing oocytes without meiotic competence represent a possible source. The mechanisms controlling the acquisition of meiotic competence, however, are still not completely clear. A gas with a short half-life, nitric oxide (NO), produced by NO-synthase (NOS) enzyme can fulfill a regulatory role in this period. The objective of this study was to ascertain the role of NO in the growth phase of pig oocytes and its influence on the acquisition of meiotic competence with the help of NOS inhibitors, NO donors and their combinations. We demonstrated that the selective competitive iNOS inhibitor aminoguanidine and also the non-selective NOS inhibitor l-NAME block meiotic maturation of oocytes with partial or even full meiotic competence at the very beginning. NOS inhibitors influence even competent oocytes in the first stage of meiotic metaphase. However, blockage is less effective than at the beginning of meiotic maturation. The number of parthenogenetically activated competent oocytes greatly increased in a pure medium after inhibitor reversion. A large quantity of NO externally added to the in vitro cultivation environment disrupts the viability of oocytes. The effectiveness of the inhibitor can be reversed in oocytes by an NO donor in a very low concentration. However, the donor is not capable of pushing the oocytes farther than beyond the first stage of meiotic metaphase. The experiments confirmed the connection of NO with the growth period and the acquisition of meiotic competence. However, it is evident from the experiments that NO is not the only stimulus controlling the growth period.

Genetic variation is vital for the populations to adapt to varying environments and to respond to artificial selection; therefore, any conservation and development scheme should start from assessing the state of variation in the population. There are several marker-based and pedigree-based parameters to describe genetic variation. The most suitable ones are rate of inbreeding and effective population size, because they are not dependent on the amount of pedigree records. The acceptable level for effective population size can be considered from different angles leading to a conclusion that it should be at least 50 to 100. The estimates for the effective population size can be computed from the genealogical records or from demographic and marker information when pedigree data are not available. Marker information could also be used for paternity analysis and for estimation of coancestries. The sufficient accuracy in marker-based parameters would require typing thousands of markers. Across breeds, diversity is an important source of variation to rescue problematic populations and to introgress new variants. Consideration of adaptive variation brings new aspects to the estimation of the variation between populations.

Sixteen purebred Iberian (IB) sows were used in two consecutive trials to determine the efficiency of conversion of sow's milk into piglet body weight (BW) gain and the relationship between milk protein and body protein retention and between milk energy yield and body energy retention in the nursing IB piglet. In each trial, four sows were selected in order to evaluate their milk production, litter growth and nutrient balance measurements, together with four additional sows for milk sampling. Litter size was equalized to six piglets. Daily milk yield (MY) was determined weekly by the weigh-suckle-weigh technique over a 34-day lactation period. Piglets were weighed individually at birth and then weekly from day 5 of lactation. Milk samples were collected on days 5, 12, 19, 26 and 34 post partum. The comparative slaughter procedure was used to determine piglet nutrient and energy retention. One piglet from each litter was slaughtered at birth and four on the morning of day 35. Total MY was on average 5.175 ± 0.157 kg/day. The average chemical composition (g/kg) of the milk was 179 ± 4 dry matter, 53.4 ± 1.0 CP, 58.5 ± 3.8 fat, 10.4 ± 0.3 ash and 56.9 ± 2.3 lactose. Milk gross energy (GE) was 4.626 ± 0.145 MJ/kg. Milk intake per piglet tended to increase in trial 2 (832 v. 893 g/day; P = 0.066). Piglet BW gain contained (g/kg) 172.1 ± 1.3 protein, 151.5 ± 3.5 fat, 41.4 ± 0.6 ash and 635 ± 3 water and 10.127 ± 0.126 MJ GE/kg. Throughout the 34-day nursing period, the piglets grew at an average rate of 168 ± 3 g/day. The ratio of daily piglet BW gain to daily MY was 0.195 ± 0.002 g/g and the gain per MJ milk GE intake was 41.9 ± 0.5 g/MJ. The overall efficiency of protein accretion (g CP gain/g CP milk intake) was low and declined in trial 2 (0.619 v. 0.571; P = 0.016). Nutrient and energy deposition between birth and weaning were 27.4 ± 0.5 g/day protein, 24.2 ± 0.8 g/day fat and 1615 ± 40 kJ/day energy. Piglet energy requirements for maintenance were 404 kJ metabolizable energy (ME)/kg BW0.75. ME was used for growth with a net efficiency of 0.584. These results suggest that poor efficiency in the use of sow's milk nutrients rather than a shortage in milk nutrient supply might explain the low growth rate of the suckling IB piglet.

‘Adapt to endure’ has become a necessity in agriculture, but the means to do so remain largely undefined. The aim of this literature review is to analyse how the herd contributes to a livestock farming system's capacity to adapt to a changing world and evolve when the future is uncertain. We identify six categories of elements linked to the herd, called ‘sources of flexibility’, that are used to manage perturbation. The first three are: using the animal's adaptive capacities, using the diversity of species and breeds and combining the diversity of animal products. The last three are: organising the mobility of animals and livestock farmers, juggling the herd numbers and mastering the balance between productivity and herd survival. These sources of flexibility are described in the literature by studying the different ways in which they are used. For example, the ‘juggle herd numbers’ source is described by volume, categories of animals, type of transfer, such as births, purchases or gifts, and timing of use, especially linked to the timing of the perturbation. Identified studies also compare or rank sources and analyse the connections between them. The flexibility framework (management science) is used for this analysis according to the levels of organisation of a livestock farming system: a strategic level referring to long-term options and to the capacity to modify the system structure, and an operational level referring to adjustment decisions during the productive cycle, the presence or absence of intervention by the livestock farmer, and the time scales involved. We conclude that the decision to use one or another source (in terms of modalities, alternatives, scheduling and combinations) is made according to the production objectives, the structural means, the type/frequency/intensity of perturbations and the context/environment. Consequently, the flexibility of a livestock farming system cannot be assessed in absolute terms. Enhancing flexibility needs management of all elements and scales involved (and not only the herd), and requires diversity to be organised at different scales.

In order to estimate the effective population size (Ne) of the current human population, two new approaches, which were derived from previous methods, were used in this study. One is based on the deviation from linkage equilibrium (LE) between completely unlinked loci in different chromosomes and another is based on the deviation from the Hardy–Weinberg Equilibrium (HWE). When random mating in a population is assumed, genetic drifts in population naturally induce linkage disequilibrium (LD) between chromosomes and the deviation from HWE. The latter provides information on the Ne of the current population, and the former provides the same when the Ne is constant. If Ne fluctuates, recent Ne changes are reflected in the estimates based on LE, and the comparison between two estimates can provide information regarding recent changes of Ne. Using HapMap Phase III data, the estimates were varied from 622 to 10 437, depending on populations and estimates. The Ne appeared to fluctuate as it provided different estimates for each of the two methods. These Ne estimates were found to agree approximately with the overall increment observed in recent human populations.

To identify mitigation options to reduce greenhouse gas (GHG) emissions from milk production (i.e. the carbon footprint (CF) of milk), this study examined the variation in GHG emissions among dairy farms using data from previous CF studies on Swedish milk. Variations between farms in these production data, which were found to have a strong influence on milk CF, were obtained from existing databases of 1051 dairy farms in Sweden in 2005. Monte Carlo (MC) analysis was used to analyse the impact of variations in seven important parameters on milk CF concerning milk yield (energy-corrected milk (ECM) produced and delivered), feed dry matter intake (DMI), enteric CH4 emissions, N content in feed DMI, N-fertiliser rate and diesel used on farm. The largest between-farm variations among the analysed production data were N-fertiliser rate (kg/ha) and diesel used (l/ha) on farm (CV = 31% to 38%). For the parameters concerning milk yield and feed DMI, the CV was approximately 11% and 8%, respectively. The smallest variation in production data was found for N content in feed DMI. According to the MC analysis, these variations in production data led to a variation in milk CF of between 0.94 and 1.33 kg CO2 equivalents (CO2e)/kg ECM, with an average value of 1.13 kg CO2e/kg ECM. We consider that this variation of ±17%, which was found to be based on the used farm data, would be even greater if all Swedish dairy farms were included, as the sample of farms in this study was not totally unbiased. The variation identified in milk CF indicates that a potential exists to reduce GHG emissions from milk production on both the national and farm levels through changes in management. As milk yield and feed DMI are two of the most influential parameters for milk CF, feed conversion efficiency (i.e. units ECM produced/unit DMI) can be used as a rough key performance indicator for predicting CF reductions. However, it must be borne in mind that feeds have different CF due to where and how they are produced.

The aim of this study was to investigate the effect of fasting and exogenous insulin administration on the expression of growth hormone receptor (GHR) and IGF-I mRNA in the pre-ovulatory follicle of ewes. Fifteen ewes received an intravaginal progesterone releasing device that was removed 6 days later (day of removal = day 0). On day −2, the ewes were divided into three groups: (i) fasting group (n = 5) that was fasted from day −2 to day 2; (ii) control group (n = 5) that received a maintenance diet; and (iii) insulin group (n = 5) that received insulin injections (0.25 IU/kg) every 12 h from day −2 to day 2 under the same diet as the control group. Follicular samples were obtained on day 2. Fasting increased plasma non-esterified fatty acids concentrations from day −1 to day 2 (P < 0.001). There was no difference (P > 0.05) in the number of follicles, although there was a tendency for an increase in the pre-ovulatory follicle diameter for the insulin group in comparison to the control group (P = 0.12). Thecal GHR mRNA expression was very low and was considered insignificant. Moreover, granulosa cells GHR mRNA expression increased (P < 0.05) in the insulin group. Expression of IGF-I mRNA was not different among groups in both tissues. In conclusion, insulin administration increases GHR mRNA but not IGF-I mRNA expression in granulosa cells of the pre-ovulatory follicle. However, fasting did not change the pattern of GHR/IGF-I mRNA expression in the pre-ovulatory follicle.